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CL-DGCN: contrastive learning based deeper graph convolutional network for traffic flow data prediction

文献类型：期刊论文

作者Zhang, Enwei1; Lv, Zhiqiang1,3; Cheng, Zesheng1; Ke, Jintao2
刊名TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
出版日期2025-11-01
卷号203页码:18
关键词Multimodal transportation Traffic flow prediction Graph convolutional network Hyperaggregation function
ISSN号1366-5545
DOI10.1016/j.tre.2025.104345
英文摘要Accurate and efficient traffic prediction helps to establish multimodal transportation systems and improve the travelling experience in daily life. Currently the mainstream traffic prediction methods are implemented based on Graph Convolutional Network (GCN), superimposing GCN layers can obtain better prediction results, but excessive superimposition will lead to the oversmooth problem, this paper proposes CL-DGCN to overcome this problem, which obtains the representations of the features through contrastive learning, and uses the improved message aggregation function to overcome the over-smooth problem. In this study, the CL-DGCN model is experimented on four domestic and international open-source, real datasets (PEMSBAY, METRLA, BEIJING and SZ-TAXI), and CL-DGCN achieves optimal or sub-optimal results in most time-step predictions, and reduces the composite error by more than 10 % compared to the baseline model, which well illustrates that the CL-DGCN model possesses more accurate prediction results.
资助项目Key Technology Research and Development Program of Shandong[2025CXGC010108] ; Shandong Province Natural Science Foundation[ZR2024MG034] ; Shandong Province Natural Science Foundation[ZR2024MF144] ; Shandong Province Natural Science Foundation[ZR2024MF142]
WOS研究方向Business & Economics ; Engineering ; Operations Research & Management Science ; Transportation
语种英语
WOS记录号WOS:001550879900002
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/41770]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cheng, Zesheng
作者单位1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China
2.Univ Hong Kong, Dept Civil Engn, Hong Kong, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Enwei,Lv, Zhiqiang,Cheng, Zesheng,et al. CL-DGCN: contrastive learning based deeper graph convolutional network for traffic flow data prediction[J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,2025,203:18.
APA Zhang, Enwei,Lv, Zhiqiang,Cheng, Zesheng,&Ke, Jintao.(2025).CL-DGCN: contrastive learning based deeper graph convolutional network for traffic flow data prediction.TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW,203,18.
MLA Zhang, Enwei,et al."CL-DGCN: contrastive learning based deeper graph convolutional network for traffic flow data prediction".TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 203(2025):18.

入库方式： OAI收割

来源：计算技术研究所

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